Abstract:

Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric
Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They
can store electrical energy from a domestic power supply and can drive the vehicle alone
in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80
% of the American driving public on average drives under 50 miles per day. A PHEV
vehicle that can drive up to 50 miles by making maximum use of cheaper electrical
energy from a domestic supply can significantly reduce the conventional fuel
consumption. This may also help in improving the environment as PHEVs emit less
harmful gases. However, the Energy Management System (EMS) of PHEVs would have
to be very different from existing EMSs of HEVs.
In this thesis, three different Energy Management Systems have been designed
specifically for PHEVs using simulated study. For most of the EMS development
mathematical vehicle models for powersplit drivetrain configuration are built and later on
the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The
main objective of the study is to design EMSs to reduce fuel consumption by the vehicle.
These EMSs are compared with existing EMSs which show overall improvement.
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In this thesis the final EMS is designed in three intermediate steps. First, a simple
rule based EMS was designed to improve the fuel economy for parametric study.
Second, an optimized EMS was designed with the main objective to improve fuel
economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to
obtain the optimum parameter values. This EMS has provided optimum parameters
which result in optimum blended mode operation of the vehicle. Finally, to obtain
optimum charge depletion and charge sustaining mode operation of the vehicle an
advanced PSO EMS is designed which provides optimal results for the vehicle to operate
in charge depletion and charge sustaining modes.
Furthermore, to implement the developed advanced PSO EMS in real-time a
possible real time implementation technique is designed using neural networks. This
neural network implementation provides sub-optimal results as compared to advanced
PSO EMS results but it can be implemented in real time in a vehicle.
These EMSs can be used to obtain optimal results for the vehicle driving conditions
such that fuel economy is improved. Moreover, the optimal designed EMS can also be
implemented in real-time using the neural network procedure described.